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https://github.com/dcs-training/network-analyisis-python
Course material for introducing data visualization with Altair and network analysis with NetworkX (in Python). Go to the readme file
https://github.com/dcs-training/network-analyisis-python
data-analysis data-visualisation network-analysis python text-analysis
Last synced: 7 days ago
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Course material for introducing data visualization with Altair and network analysis with NetworkX (in Python). Go to the readme file
- Host: GitHub
- URL: https://github.com/dcs-training/network-analyisis-python
- Owner: DCS-training
- License: other
- Created: 2022-04-08T17:52:58.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-29T09:17:40.000Z (4 months ago)
- Last Synced: 2024-07-29T12:31:19.377Z (4 months ago)
- Topics: data-analysis, data-visualisation, network-analysis, python, text-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 12.8 MB
- Stars: 0
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Visualization and Network Analysis in Python: Altair and NetworkX
Course material for intermediate Python programmers, covering data visualization with Altair and network analysis with NetworkXMaterial created by Lucy Havens for the [Centre for Data, Culture, & Society](http://cdcs.ed.ac.uk)
Folder contents:
* Slides: PDF documents, two per week
* Notebooks: Jupyter Notebooks demoing content from the slides and completing the assignments
* Assignments: PDF documents, one per weekThe material in this repo is licensed under [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).
**Week 1:** Network Analysis Analyzing network data using NetworkX
**Week 2:** Data Visualization Creating data visualizations using Altair (with pandas)
## How to use the Jupyter Notebooks
### 1. Run the notebooks via GoogleColab
Open Google Colab: [https://colab.research.google.com](https://colab.research.google.com)
If you are not already logged you will be prompted to log-in via Gmail#### Upload the Notebook to Google Colab
1. Go to the GitHub header and copy and paste the link to this repo and select the notebook you want to use and press enter### Using the Notebook
The Notebook contains paragraphs of explanatory text interspersed with grey cells containing code blocks. To run a code block and see the result:1. Place your cursor within the cell
2. Click the 'Run' button on the top menu
4. The results of running this code will appear below
5. If the results don't appear immediately, check the icon in the browser tab. AN egg-timer icon indicates it is processing the code.
6. It is best to follow the Notebook from top to bottom as some code blocks will depend on results from previous cells
7. You can edit code blocks yourself and run them to see the results of your changes
### Clearing the cells
To clear the results and run the code again you can use the 'Cell' menu on the top menu bar1. To clear the results of the current cell: **Cell > Current Outputs > Clear**
2. To clear the results of all cells: **Cell > All Output > Clear**### 2. Installing Python via Anaconda
[Python][python] is great for general-purpose programming and is a popular language for scientific computing as well. Installing all of the packages required for this lessons individually can be a bit difficult, however, so we recommend the all-in-one installer [Anaconda][anaconda].
Regardless of how you choose to install it, please make sure you install Python version 3.x (e.g., Python 3.6 version).
#### Windows - [Video tutorial][video-windows]
1. Open [anaconda.com/download][anaconda-dl] with your web browser.
2. Download the Python 3 installer for Windows.
3. Double-click the executable and install Python 3 using _MOST_ of the default settings. The only exception is to check the **Make Anaconda the default Python** option.
#### macOS - [Video tutorial][video-mac]
1. Open [anaconda.com/download][anaconda-dl] with your web browser.
2. Download the Python 3 installer for macOS.
3. Install Python 3 using all of the defaults for installation.
#### Starting Python
To start Jupyter Notebook Open the Anaconda Navigator and Launch Jupyter Notebook#### Upload the Notebook
1. Download the notebook on your machine
2. Go to Upload
3. Navigate to where you have downloaded your file
4. Select Upload again
5. Double-click on the uploaded file### 3. Run the notebook in Noteable
If you are part of the University of Edinburgh you can use [Noteable](https://noteable.edina.ac.uk/) the cloud-based computational notebook system that work on your browser from any device.
To get started:
#### Get the Notebook files for this tutorial
Download the files listed on the right to a location on your computer
Make sure you know the location they have been downloaded to (usually your 'Downloads' folder) as you will need to upload them to Noteable. (The filename should end with '.ipynb'. If your computer has appended '.txt' to the end of the file make sure this is removed)#### Start Noteable
1. Open the following link in a new tab: [https://noteable.edina.ac.uk/login](https://noteable.edina.ac.uk/login)
2. Login with your EASE credentials
3. Under 'Standard Notebook' click 'Start'
#### Upload the Notebook to Noteable
1. From the Noteable home page, click on the 'Upload' button at the top right of the screen and browse to one of the files you saved earlier to select it.
2. Now click the blue 'Upload' button to load it into Noteable
3. Once the file has been uploaded, click on the filename to start the Notebook[anaconda]: https://www.anaconda.com/distribution
[anaconda-dl]: https://www.anaconda.com/download/
[python]: https://python.org
[jupyter]: https://jupyter.org/index.html
[jupyter-install]: https://jupyter.org/install.html
[video-mac]: https://www.youtube.com/watch?v=TcSAln46u9U
[video-windows]: https://www.youtube.com/watch?v=xxQ0mzZ8UvA